PROJECT SUMMARY Recent advances in light and electron microscopy, along with new probes and labeling methods for cellular and molecular imaging, are driving a revolutionary expansion in biomedical research activities that involve exploration of complex biological systems. These new capabilities in instruments and methods are providing nearly seamless views across multiple scales of resolution of biological specimens. At the same time, researchers are increasingly generating extremely large and diverse datasets of enormous value, which are driving renewed demand for software and tools to facilitate aggregation, curation, refinement, and spatial and semantic ?assembly? of these data. A key consumer of these data is the computational modeling and simulations community, which requires more accurate in-silico representations of supramolecular complexes and organelles in the context of complete cells and cells deployed in tissues to better model the impact of molecular changes found in diseases on the functioning of a cell. Building on prior success, we propose to continue to support, maintain, and extend the capabilities of the Cell Centered Database, which we have recently renamed the Cell Image Library (CIL). The CIL is a cell- centered community repository for storing, managing, and sharing multi-scale microscopy data encompassing cellular networks, cellular and subcellular microdomains, and their macromolecular components. The CIL is comprised of software for researchers to upload, organize, process, and share project data prior to publication. It also provides a searchable, public-facing website for users to disseminate their results and openly distribute their data for reuse by others. Our extended development plans include integration of computational workflows to facilitate generation of fully segmented, 3D structural models ready for meshing, discretization, and use in assembling more complete, realistic, and accurate models for simulations. We will also harden and extend our unique capabilities for supporting large data, in particular massive individual 3D datasets, to enable biomedical researchers to not only store, but to process and visualize their data at scale with tools that are easy to use and easily accessible.